Human and numerical observer studies of lesion detection in Ga-67 images obtained with MAP-EM reconstructions and anatomical priors

Regularization can be implemented in iterative image reconstruction by using an algorithm such as Maximum-A-Posteriori Ordered-Subsets-Expectation-Maximization (MAP OSEM) which favors a smoother image as the solution. One way of controlling the smoothing is to introduce, during the reconstruction process, a prior knowledge about the slice anatomy. In a previous work, we showed using numerical observers that anatomical priors can improve lesion detection accuracy in simulated Ga-67 images of the chest. The goal of this work is to expand and enhance our previous investigations by conducting human-observer localization receiver observer characteristics (LROC) studies and to compare the results to those of a multiclass channelized non-prewhitening (CNPW) model observer. Phantom images were created using the SIMIND Monte Carlo simulation software from the MCAT phantom. The lesion: background contrast was 27.5:1. The anatomical data employed were the structure boundaries from the original, noise-free slices of the MCAT phantom. Images were reconstructed using the DePierro MAP algorithm with surrogate functions. Images were also reconstructed with no priors using the RBI-EM algorithm, with 4 iterations and 4 projections per subset Two weights (0.005 and 0.04) for the prior were tested. The following reconstruction scheme was used to reach convergence for the anatomical priors: The 120 projections were reconstructed successively with 4, 8, 24, 60, and 120 projections per subset with 1, 1, 1, 1, and finally 50 iterations respectively; the result of each reconstruction was used as an initial estimate for the next reconstruction. The human observer areas-under-the-curves (AUC´s) agreed with the numerical observer in ranking use of organ and lesion boundaries highest, a slight decrease with tumor boundaries present when no functional tumor was present, and a further slight decrease when just organ boundaries were employed